Schizophrenia remains one of the most devastating chronic illnesses,1 impacting nearly 1.5% of the global population2 and creating an economic burden of up to 1.65% gross domestic product.3 It is not surprising that digital tools for schizophrenia, often smartphone-based software in the form of apps, have received so much recent attention and enthusiasm. Digital phenotyping holds tremendous potential in elucidating the complex heterogeneity of what we call schizophrenia and would therefore advance research. On a clinical front, smartphone data may become increasingly valuable in monitoring course and treatment response given the fact that these patients frequently have difficulties in adherence with clinical visits, and are often poor historians. However, the power of this paradigm is currently fueled more by the increasing ubiquity of technology than breakthroughs in clinical science. The accessibility and affordability of digital care derives from increasing global ownership of smartphones: it is estimated that six billion smartphones will be in circulation worldwide by 2020.4 As devices and sensors become ever cheaper and more sophisticated, the ability to capture a plethora of relevant data and deliver a myriad of content via network connectivity will continue to fuel the potential of digital approaches in mental health. As validation and reproducibility lags behind enthusiasm and availability, the potential clinical impact of these digital tools is at a crossroads.
How might this new approach advance clinical care? Affordable and accurate diagnostics from smartphones paired with on-demand or automatically deployed interventions enables unprecedented access to mental health services. Apps today are designed to perform a wide range of healthcare tasks ranging from telehealth to medication tracking. In addition, new platforms are currently being developed to measure novel behavioral and physiological markers using passive long-term smartphone data, enabling objective measurement without burden for patients and healthcare providers. Clinically relevant and passively collected smartphone data comprises a wide range of sensors, including accelerometer data to estimate activity, anonymized call/text log information to estimate sociability, and screen touch data to estimate cognition. Passive measurement might also be able to distinguish disease subtypes to help better classify psychotic illnesses, similar to recent research using genetic, physiological, and cognitive markers.5